2+ Glue semantics Jobs in Bangalore (Bengaluru) | Glue semantics Job openings in Bangalore (Bengaluru)

ROLES AND RESPONSIBILITIES:

You will be responsible for architecting, implementing, and optimizing Dremio-based data Lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.

• Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/Databricks ecosystem).
• Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads.
• Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns.
• Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS).
• Establish best practices for data security, lineage, and access control aligned with enterprise governance policies.
• Support self-service analytics by enabling governed data products and semantic layers.
• Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling.
• Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data.

IDEAL CANDIDATE:

• Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
• 5+ years in data architecture and engineering, with 3+ years in Dremio or modern lakehouse platforms.
• Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena).
• Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning.
• Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.).
• Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC).
• Excellent problem-solving, documentation, and stakeholder communication skills.

PREFERRED:

• Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) and data catalogs (Collibra, Alation, Purview).
• Exposure to Snowflake, Databricks, or BigQuery environments.
• Experience in high-tech, manufacturing, or enterprise data modernization programs.

Job Overview:

We are seeking an experienced AWS Data Engineer to join our growing data team. The ideal candidate will have hands-on experience with AWS Glue, Redshift, PySpark, and other AWS services to build robust, scalable data pipelines. This role is perfect for someone passionate about data engineering, automation, and cloud-native development.

Key Responsibilities:

• Design, build, and maintain scalable and efficient ETL pipelines using AWS Glue, PySpark, and related tools.
• Integrate data from diverse sources and ensure its quality, consistency, and reliability.
• Work with large datasets in structured and semi-structured formats across cloud-based data lakes and warehouses.
• Optimize and maintain data infrastructure, including Amazon Redshift, for high performance.
• Collaborate with data analysts, data scientists, and product teams to understand data requirements and deliver solutions.
• Automate data validation, transformation, and loading processes to support real-time and batch data processing.
• Monitor and troubleshoot data pipeline issues and ensure smooth operations in production environments.

Required Skills:

• 5 to 7 years of hands-on experience in data engineering roles.
• Strong proficiency in Python and PySpark for data transformation and scripting.
• Deep understanding and practical experience with AWS Glue, AWS Redshift, S3, and other AWS data services.
• Solid understanding of SQL and database optimization techniques.
• Experience working with large-scale data pipelines and high-volume data environments.
• Good knowledge of data modeling, warehousing, and performance tuning.

Preferred/Good to Have:

• Experience with workflow orchestration tools like Airflow or Step Functions.
• Familiarity with CI/CD for data pipelines.
• Knowledge of data governance and security best practices on AWS.